CPSNet: a cyclic pyramid-based small lesion detection network

The presence of small lesions is an important marker for determining whether a patient will develop malignant tumors. Clinical practitioners could easily overlook the presence of small lesions, meaning automated approaches are essential for screening test results. The use of deep learning-based detectors for this purpose has so far been suboptimal as small lesions easily lose the spatial information during the convolution operation, resulting in unsatisfactory detection accuracy and limited application in clinical decision making. In this paper, we propose a Cyclic Pyramid-based Small lesion detection Network (CPSNet), which iteratively enhances the features in the parallel layer of the Feature Parallel Network (FPN), the features learned in the loop are fused again with the initial FPN to compensate for the inadequacy problem in the initial training. In addition, we propose an aggregated dilation block (ADB) to capture small variations at different scales and a global attention block (GAB) to adaptively recalibrate the channel-based feature responses while focusing on the target spatial information and highlighting the most relevant feature channels. Extensive experiments on eight organs included in the DeepLesion dataset show that our method has a high detection accuracy(mAP=60.4) and a high overall sensitivity(80.5%), which is superior to the state-of-art methods.